AliAmini93/Telecom-Churn-Analysis
Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.
This project helps telecom companies understand why customers leave by analyzing their usage patterns and demographics. It takes customer data like age, contract type, and service usage, and predicts which customers are likely to churn. This allows customer retention specialists or marketing managers to proactively target at-risk customers with specific offers to keep them.
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Use this if you are a telecom manager who needs to identify customers at high risk of canceling their service and wants to understand the key factors driving that decision.
Not ideal if you are looking for a system that provides real-time, automated customer interventions or if your primary concern is predicting customer lifetime value rather than churn risk.
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Last pushed
Aug 12, 2024
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